This paper presents the design of a small printed ultra wideband antenna with Band Notched characteristics. Both the free space and on-body performances of this antenna were investigated through simulation. The newly ...This paper presents the design of a small printed ultra wideband antenna with Band Notched characteristics. Both the free space and on-body performances of this antenna were investigated through simulation. The newly designed UWB antenna is more revised small form factor sized, with the ability to avoid interference caused by WLAN (5.15 - 5.825 GHz) and WiMAX (5.25 - 5.85 GHz) systems with a band notch. The return loss response, gain, radiation pattern on free space of the antenna were investigated. After that, the on-body performances were tested on 3-layer human body model with radiation pattern, gain, return loss, and efficiency at 3.5, 5.7, 8, 10 GHz and all the results were compared with free space results. As the on-body performance was very good, the proposed antenna will be suitable to be used for multi-purpose medical applications and sports performance monitoring.展开更多
基于散射中心参数化模型和反向传播(back propagation,BP)神经网络,构建了一种针对目标全角度、宽频段下的远场电场预测网络,该网络将利用目标的位置、幅度、频率等数据信息实现远场电场实部与虚部的快速预测.首先,将对目标强散射点的...基于散射中心参数化模型和反向传播(back propagation,BP)神经网络,构建了一种针对目标全角度、宽频段下的远场电场预测网络,该网络将利用目标的位置、幅度、频率等数据信息实现远场电场实部与虚部的快速预测.首先,将对目标强散射点的位置以及强度等参数进行提取;然后,对二维角域以及频域进行区域划分,构建并联式的智能网络架构,从而建立散射中心参数化模型与高精度远场电场间的关系.该方法能够通过新型并联网络的训练,减小传统散射中心模型的频率、角度依赖性的影响,实现目标远场电场的快速获取.由于在网络设计时,充分借鉴了现有的模型中各散射参数对目标电场的影响,因此该神经网络具有清晰的物理意义以及突出的泛化能力.与传统的基于几何绕射理论(geometrical theory of diffraction,GTD)模型的电场重构方法相比,本文方法具有更高的准确性,实验结果表明提出的并联网络使得预测电场误差下降了18%以上,同时针对目标后向远场电场的预测,其相对均方根误差能够小于5%.展开更多
文摘This paper presents the design of a small printed ultra wideband antenna with Band Notched characteristics. Both the free space and on-body performances of this antenna were investigated through simulation. The newly designed UWB antenna is more revised small form factor sized, with the ability to avoid interference caused by WLAN (5.15 - 5.825 GHz) and WiMAX (5.25 - 5.85 GHz) systems with a band notch. The return loss response, gain, radiation pattern on free space of the antenna were investigated. After that, the on-body performances were tested on 3-layer human body model with radiation pattern, gain, return loss, and efficiency at 3.5, 5.7, 8, 10 GHz and all the results were compared with free space results. As the on-body performance was very good, the proposed antenna will be suitable to be used for multi-purpose medical applications and sports performance monitoring.
文摘基于散射中心参数化模型和反向传播(back propagation,BP)神经网络,构建了一种针对目标全角度、宽频段下的远场电场预测网络,该网络将利用目标的位置、幅度、频率等数据信息实现远场电场实部与虚部的快速预测.首先,将对目标强散射点的位置以及强度等参数进行提取;然后,对二维角域以及频域进行区域划分,构建并联式的智能网络架构,从而建立散射中心参数化模型与高精度远场电场间的关系.该方法能够通过新型并联网络的训练,减小传统散射中心模型的频率、角度依赖性的影响,实现目标远场电场的快速获取.由于在网络设计时,充分借鉴了现有的模型中各散射参数对目标电场的影响,因此该神经网络具有清晰的物理意义以及突出的泛化能力.与传统的基于几何绕射理论(geometrical theory of diffraction,GTD)模型的电场重构方法相比,本文方法具有更高的准确性,实验结果表明提出的并联网络使得预测电场误差下降了18%以上,同时针对目标后向远场电场的预测,其相对均方根误差能够小于5%.